cosmomvpa.org

CoSMoMVPA — CoSMo Multivariate Pattern Analysis toolbox 1.0rc1 documentation

A multi-modal, multi-variate pattern analysis (MVPA) toolbox in Matlab / GNU Octave for cognitive neuroscientists. State-of-the art, yet simple to use MVPA implementations. Runs on the Matlab and GNU Octave platform, on MS Windows, OSX, and GNU/Linux. Handles fMRI volumetric, fMRI surface-based, and MEEG data through a uniform data structure. Support for a wide range of data formats. Searchlights in the volume, on the surface, over sensors, time bins, and frequency bands. FMRI ROI split-half correlations.

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CoSMoMVPA — CoSMo Multivariate Pattern Analysis toolbox 1.0rc1 documentation | cosmomvpa.org Reviews

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A multi-modal, multi-variate pattern analysis (MVPA) toolbox in Matlab / GNU Octave for cognitive neuroscientists. State-of-the art, yet simple to use MVPA implementations. Runs on the Matlab and GNU Octave platform, on MS Windows, OSX, and GNU/Linux. Handles fMRI volumetric, fMRI surface-based, and MEEG data through a uniform data structure. Support for a wide range of data formats. Searchlights in the volume, on the surface, over sensors, time bins, and frequency bands. FMRI ROI split-half correlations.

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CoSMoMVPA — CoSMo Multivariate Pattern Analysis toolbox 1.0rc1 documentation | cosmomvpa.org Reviews

https://cosmomvpa.org

A multi-modal, multi-variate pattern analysis (MVPA) toolbox in Matlab / GNU Octave for cognitive neuroscientists. State-of-the art, yet simple to use MVPA implementations. Runs on the Matlab and GNU Octave platform, on MS Windows, OSX, and GNU/Linux. Handles fMRI volumetric, fMRI surface-based, and MEEG data through a uniform data structure. Support for a wide range of data formats. Searchlights in the volume, on the surface, over sensors, time bins, and frequency bands. FMRI ROI split-half correlations.

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cosmomvpa.org

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test average samples — CoSMo Multivariate Pattern Analysis toolbox 1.0rc1 documentation

http://www.cosmomvpa.org/matlab/test_average_samples.html

CoSMoMVPA unit tests - complete files. Tests for cosmo average samples. See the COPYING file distributed with CoSMoMVPA. #. No mixing of different targets. No mixing of different targets. Test average samples split by. Test average samples split by empty. Test average samples exceptions. Test average samples with repeats. Select subset of samples, each with at least ncombi min repeats. Ensure at least one with minimum. Bit widths for features, chunks, targets, and repeats. Chunk, target, repeat count.

2

Tips and tricks — CoSMo Multivariate Pattern Analysis toolbox 1.0rc1 documentation

http://www.cosmomvpa.org/tips.html

Advanced Matlab / Octave concepts. General tips and tricks. Advanced Matlab / Octave concepts. A logical mask is an array of type. And can only contain values. Array, and values in this array can be indexed as other (e.g. numeric, cell) arrays. For example,. Returns the array in the third colum, that is the. Note that when showing a logical array, Matlab / Octave do not show. Respectively. To see whether a variable. Is a logical array, run. Can be used to index another array. That contains the elements of.

3

test fmri dataset — CoSMo Multivariate Pattern Analysis toolbox 1.0rc1 documentation

http://www.cosmomvpa.org/matlab/test_fmri_dataset.html

CoSMoMVPA unit tests - complete files. Tests for cosmo fmri dataset. See the COPYING file distributed with CoSMoMVPA. #. Test base fmri dataset. Test afni fmri dataset. Cosmo skip test if no external. Afni expected no img. Afni expected no img. Test nii sform fmri dataset. Get expected nii sform. Test nii qform fmri dataset. Get expected nii qform. Check qform with illegal pixdims. Test nii pixdim fmri dataset. Get expected nii pixdim. Test nii sform afni 5d singleton dataset. Get expected nii sform.

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CoSMoMVPA functions - complete files — CoSMo Multivariate Pattern Analysis toolbox 1.0rc1 documentation

http://www.cosmomvpa.org/modindex.html

CoSMoMVPA functions - complete files. Load an fmri volumetric dataset. Maps a dataset structure to a NIFTI, AFNI, or BV structure or file. Returns a dataset structure based on MEEG data. Maps a dataset to a FieldTrip or EEGlab structure or file. Returns a dataset structure based on surface mesh data. Maps a dataset structure to AFNI/SUMA NIML dset or BV SMP file. Check consistency of a dataset. Slice a dataset by samples (the default) or features. Stacks multiple datasets to yield a single dataset. Cosmo...

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cosmo sample unique — CoSMo Multivariate Pattern Analysis toolbox 1.0rc1 documentation

http://www.cosmomvpa.org/matlab/cosmo_sample_unique.html

CoSMoMVPA functions - complete files. K,n,count,varargin ). Sample without replacement from subsets of integers in balanced manner. Function samples=cosmo sample unique(k,n[,count,varargin]). K number of elements to return in each subset. N size of integer range from which to sample. Count number of subsets to select (default: 1). Seed, s Use seed s for pseudo-random sampling (optional). If this. Option is omitted, then different calls to this function. May (usually: will) return different results. Outpu...

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mvpa.blogspot.com

MVPA Meanderings: May 2015

http://mvpa.blogspot.com/2015_05_01_archive.html

Musings on fMRI multivariate pattern analyses. Monday, May 18, 2015. Resampling images with wb command -volume-affine-resample. I often need to resample images without performing other calculations, for example, making a 3x3x3 mm voxel version of an anatomical image with 1x1x1 mm voxels for use as an underlay. This can be done with ImCalc. But that's a bit annoying, as it requires firing up SPM, and only outputs two-part NIfTI images (minor annoyances, but still). The wb command -volume-affine-resample.

mvpa.blogspot.com

MVPA Meanderings: MVPA on the surface: to interpolate or not to interpolate?

http://mvpa.blogspot.com/2015/05/mvpa-on-surface-to-interpolate-or-not.html

Musings on fMRI multivariate pattern analyses. Friday, May 15, 2015. MVPA on the surface: to interpolate or not to interpolate? A few weeks ago I posted. About a set of ROI-based MVPA results using HCP images. Comparing the results of doing the analysis with the surface or volume version of the dataset. As mentioned there, there hasn't been a huge amount of MVPA with surface data, but there has been some, particularly using the algorithms in Surfing. They're also in pyMVPA. Described by Nikolaas Oosterhof.

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